A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis

Seven million people suffer complications after surgery each year. With sufficient surgical training and feedback, half of these complications could be prevented. Automatic surgical video analysis, especially for minimally invasive surgery, plays a key role in training and review, with increasing interests from recent studies on tool and workflow detection. In this research, a novel machine learning architecture, RPM-CNN, is created to perform real-time surgical video analysis. This architecture, for the first time, integrates visual simultaneous localization and mapping (vSLAM) into Mask R-CNN. Spatio-temporal information, in addition to the visual features, is utilized to increase the accuracy to 96.8 mAP for tool detection and 97.5 mean Jaccard for workflow detection, surpassing all previous works via the same benchmark dataset. As a real-time prediction, the RPM-CNN model reaches a 50 FPS runtime performance speed, 10x faster than region based CNN, by modeling the spatio-temporal information directly from surgical videos during the vSLAM 3D mapping. Additionally, this novel Region Proposal Module (RPM) replaces the region proposal network (RPN) in Mask R-CNN, accurately placing bounding-boxes and lessening the annotation requirement. In principle, this architecture integrates the best of both worlds, inclusive of (1) vSLAM on object detection, through focusing on geometric information for region proposals and (2) CNN on object recognition, through focusing on semantic information for image classification; the integration of these two technologies into one joint training process opens a new door in computer vision. Furthermore, to apply RPM-CNN's real-time top performance to the real world, a Microsoft HoloLens 2 application is developed to provide an augmented reality (AR) based solution for both surgical training and assistance.